Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications

Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications

Measurements, Models, Systems and Design

531 s.

Anomaly detection & object classification using multi-spectral LiDAR and sonar

In this thesis, we present the theory of high-dimensional signal approximation of multifrequency signals. We also present both linear and non-linear compressive sensing (CS) algorithms that generate encoded representations of time-correlated single photon counting (TCSPC) light detection and ranging (LiDAR) data, side-scan sonar (SSS) and synthetic aperture sonar (SAS). The main contributions of this thesis are summarised as follows: 1. Research is carried out studying full-waveform (FW) LiDARs, in particular, the TCSPC data, capture, storage and processing. 2. FW-LiDARs are capable of capturing large quantities of photon-counting data in real-time. However, the real-time processing of the raw LiDAR waveforms hasn’t been widely exploited. This thesis answers some of the fundamental questions: • can semantic information be extracted and encoded from raw multi-spectral FW-LiDAR signals? • can these encoded representations then be used for object segmentation and classification? 3. Research is carried out into signal approximation and compressive sensing techniques, its limitations and the application domains. 4. Research is also carried out in 3D point cloud processing, combining geometric features with material spectra (spectral-depth representation), for object segmentation and classification. 5. Extensive experiments have been carried out with publicly available datasets, e.g. the Washington RGB Image and Depth (RGB-D) dataset [108], YaleB face dataset1 [110], real-world multi-frequency aerial laser scans (ALS)2 and an underwater multifrequency (16 wavelengths) TCSPC dataset collected using custom-build targets especially for this thesis. 6. The multi-spectral measurements were made underwater on targets with different shapes and materials. A novel spectral-depth representation is presented with strong discrimination characteristics on target signatures. Several custom-made and realistically scaled exemplars with known and unknown targets have been investigated using a multi-spectral single photon counting LiDAR system. 7. In this work, we also present a new approach to peak modelling and classification for waveform enabled LiDAR systems. Not all existing approaches perform peak modelling and classification simultaneously in real-time. This was tested on both simulated waveform enabled LiDAR data and real ALS data2 . This PhD also led to an industrial secondment at Carbomap, Edinburgh, where some of the waveform modelling algorithms were implemented in C++ and CUDA for Nvidia TX1 boards for real-time performance. 1http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ 2This dataset was captured in collaboration with Carbomap Ltd. Edinburgh, UK. The data was collected during one of the trials in Austria using commercial-off-the-shelf (COTS) sensors

Characterization of epitaxial silicene on the Ag(111) surface with synchrotron-based soft X-ray spectroscopy

The eventual incorporation of two-dimensional materials into electronic devices will require an intimate understanding of their electronic properties, as well as how these properties are affected by the material's local environment. The specific focus of this thesis is on silicene, the silicon-based analogue to the prototypical 2D material graphene. In particular, the studies contained within this thesis aim to elucidate the electronic properties of silicene monolayers and multilayers grown on the Ag(111) surface through combination of ab initio density functional theory calculations and synchrotron-based soft X-ray spectroscopy. The first study explores the electronic interaction between epitaxial silicene monolayers and their Ag(111) substrate, finding evidence for significant hybridization between them that confers a metallic electronic structure upon the silicene. The second examines the effects of oxidation on the electronic and structural characteristics of epitaxial silicene monolayers on Ag(111), refuting the notion of a bandgap opening at low oxygen coverage and showing that the structure is inherently unstable at high oxygen coverage. Finally, silicene growth beyond a monolayer is studied, and strong evidence for the instability of multilayer silicene on Ag(111) is presented. In addition, this thesis contains a review of the history, properties and potential applications of a variety of two-dimensional materials, focusing on the qualities that will impact their application to electronic devices. It also discusses the techniques involved in producing, structurally characterizing, and predicting and measuring the electronic properties of epitaxial silicene on Ag(111)

PREDICTION AND CONTROL OF IMAGE PROPERTIES IN ADVANCED COMPUTED TOMOGRAPHY

Computed Tomography (CT) is an important technique that is in widespread use for disease diagnosis, monitoring, and interventional procedures. There are many varieties of CT including cone-beam CT (CBCT) that has exceptional high spatial resolution and spectral CT that incorporates energy-dependent measurements for advanced material discrimination. The goal of this research is to quantify image properties using a prospective prediction framework for advanced reconstruction in CBCT and spectral CT systems. These predictors analyze the dependencies of image properties on system configuration, acquisition strategy, and reconstruction regularization design. The prospective estimation of image properties facilitates novel system and acquisition design, adaptive and task-driven imaging, and tuning of regularization for robust and reliable performance. The proposed research quantifies the image properties of model-based iterative reconstruction (MBIR) in CBCT and model-based material decomposition (MBMD) in spectral CT, including spatial resolution, the generalized response to local perturbations, and noise correlation. Predictions are derived with a realistic system model including physical blur, noise correlation, and a poly-energetic model that applies to a variety of spectral CT protocols. Reconstruction methods combining data statistical fidelity and various advanced regularization designs are explored. Prediction accuracy is validated with measured image properties in both simulation and physical experiments. The theoretical understanding is applied to applications with prospective reconstruction regularization design

Real-time spectral modelling of audio for creative sound transformation

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Towards Predictive Rendering in Virtual Reality

The strive for generating predictive images, i.e., images representing radiometrically correct renditions of reality, has been a longstanding problem in computer graphics. The exactness of such images is extremely important for Virtual Reality applications like Virtual Prototyping, where users need to make decisions impacting large investments based on the simulated images. Unfortunately, generation of predictive imagery is still an unsolved problem due to manifold reasons, especially if real-time restrictions apply. First, existing scenes used for rendering are not modeled accurately enough to create predictive images. Second, even with huge computational efforts existing rendering algorithms are not able to produce radiometrically correct images. Third, current display devices need to convert rendered images into some low-dimensional color space, which prohibits display of radiometrically correct images. Overcoming these limitations is the focus of current state-of-the-art research. This thesis also contributes to this task. First, it briefly introduces the necessary background and identifies the steps required for real-time predictive image generation. Then, existing techniques targeting these steps are presented and their limitations are pointed out. To solve some of the remaining problems, novel techniques are proposed. They cover various steps in the predictive image generation process, ranging from accurate scene modeling over efficient data representation to high-quality, real-time rendering. A special focus of this thesis lays on real-time generation of predictive images using bidirectional texture functions (BTFs), i.e., very accurate representations for spatially varying surface materials. The techniques proposed by this thesis enable efficient handling of BTFs by compressing the huge amount of data contained in this material representation, applying them to geometric surfaces using texture and BTF synthesis techniques, and rendering BTF covered objects in real-time. Further approaches proposed in this thesis target inclusion of real-time global illumination effects or more efficient rendering using novel level-of-detail representations for geometric objects. Finally, this thesis assesses the rendering quality achievable with BTF materials, indicating a significant increase in realism but also confirming the remainder of problems to be solved to achieve truly predictive image generation

Matlab

This book is a collection of 19 excellent works presenting different applications of several MATLAB tools that can be used for educational, scientific and engineering purposes. Chapters include tips and tricks for programming and developing Graphical User Interfaces (GUIs), power system analysis, control systems design, system modelling and simulations, parallel processing, optimization, signal and image processing, finite different solutions, geosciences and portfolio insurance. Thus, readers from a range of professional fields will benefit from its content